Prompt Tornado evaluates every release on two axes — does it plan and route the workflow correctly, and is each step's output actually good — and blocks deploys on quality regressions. Below is the methodology and the current numbers from the internal evaluation report.
Figures from Prompt Tornado's internal evaluation harness. Task-type quality is scored 1–10 by an independent LLM judge against per-task rubrics.
The failure mode nobody catches is a workflow that keeps running while its output slowly gets worse. Models update, prompts drift, a provider swaps in a fallback — and the pipeline returns something plausible but wrong. Evaluation exists to catch that before your users do.
The planner was evaluated on 200 compound prompts representing real-world AI workflows — checking for valid schema, correct step ordering, and no hallucinated tasks. It also produces 100% deterministic plans: identical prompts yield identical plans.
Each of the 181 registry task types was executed and scored by an independent LLM judge against task-specific rubrics. Media-generation categories (image, audio, video) are validated structurally rather than judge-scored.
The evaluation harness, rubrics, and CI quality gate are part of the platform. A change that drops below the quality bar is automatically blocked from shipping — evaluation isn't a report you read after the fact, it's a gate in the pipeline.
181 task types across 17 categories. "Routing" is how many cases were sent to the correct model. Media categories are structurally validated, so they carry no judge score.
| Category | Cases | Avg quality | Routing |
|---|---|---|---|
| Text Generation | 31 | 8.97 | 28/31 |
| Specialized Domains | 18 | 8.83 | 17/18 |
| Code Generation | 17 | 8.41 | 16/17 |
| Data & Analysis | 14 | 8.31 | 13/14 |
| Agentic / Automation | 11 | 6.82 | 11/11 |
| Research | 10 | 7.70 | 10/10 |
| Summarization | 9 | 9.11 | 9/9 |
| Image Generation | 9 | — | 9/9 |
| Reasoning & Planning | 9 | 9.00 | 8/9 |
| Question Answering | 8 | 8.00 | 8/8 |
| Content Editing | 8 | 8.62 | 8/8 |
| Audio Generation | 7 | — | 7/7 |
| Vision / Multimodal | 7 | 8.00 | 7/7 |
| Translation | 6 | 6.83 | 6/6 |
| Video Generation | 6 | — | 6/6 |
| Structured Output | 6 | 9.33 | 5/6 |
| Personalization | 5 | 8.25 | 4/5 |
| All task types | 181 | 8.43 | 172/181 |
Source: Prompt Tornado internal AI Workflow Evaluation Report, quality_v1 baseline (2026-05-11). Read the full report →
Compare current behavior against a known-good baseline. When you change a prompt, add a task type, or a model version ships, regression checks tell you whether the change held or quietly broke something.
A quality bar a change must clear before it goes live. If it regresses, it doesn't ship. Gating routing changes on evaluations is what lets routing evolve without rotting.
Every run is recorded — input, each task, model, provider, latency, tokens, cost, status, and any fallback. "The AI got it wrong" becomes "step 2 fell back at 14:02."
Fallbacks are healthy until they're constant. Monitoring surfaces a failing primary model as a pattern — a degrading provider or misconfigured key — before it becomes an unexplained quality problem.
Regression checks, evaluation gates, and full run traces on every workflow.